Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918037483945984 |
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| author | Wang, Chaoqi Zhao, Zhuokai Jiang, Yibo Chen, Zhaorun Zhu, Chen Chen, Yuxin Liu, Jiayi Zhang, Lizhu Fan, Xiangjun Ma, Hao Wang, Sinong |
| author_facet | Wang, Chaoqi Zhao, Zhuokai Jiang, Yibo Chen, Zhaorun Zhu, Chen Chen, Yuxin Liu, Jiayi Zhang, Lizhu Fan, Xiangjun Ma, Hao Wang, Sinong |
| contents | Recent advances in large language models (LLMs) have demonstrated significant progress in performing complex tasks. While Reinforcement Learning from Human Feedback (RLHF) has been effective in aligning LLMs with human preferences, it is susceptible to spurious correlations in reward modeling. Consequently, it often introduces biases-such as length bias, sycophancy, conceptual bias, and discrimination-that hinder the model's ability to capture true causal relationships. To address this, we propose a novel causal reward modeling approach that integrates causality to mitigate these spurious correlations. Our method enforces counterfactual invariance, ensuring reward predictions remain consistent when irrelevant variables are altered. Through experiments on both synthetic and real-world datasets, we show that our approach mitigates various types of spurious correlations effectively, resulting in more reliable and fair alignment of LLMs with human preferences. As a drop-in enhancement to the existing RLHF workflow, our causal reward modeling provides a practical way to improve the trustworthiness and fairness of LLM finetuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09620 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment Wang, Chaoqi Zhao, Zhuokai Jiang, Yibo Chen, Zhaorun Zhu, Chen Chen, Yuxin Liu, Jiayi Zhang, Lizhu Fan, Xiangjun Ma, Hao Wang, Sinong Machine Learning Artificial Intelligence Recent advances in large language models (LLMs) have demonstrated significant progress in performing complex tasks. While Reinforcement Learning from Human Feedback (RLHF) has been effective in aligning LLMs with human preferences, it is susceptible to spurious correlations in reward modeling. Consequently, it often introduces biases-such as length bias, sycophancy, conceptual bias, and discrimination-that hinder the model's ability to capture true causal relationships. To address this, we propose a novel causal reward modeling approach that integrates causality to mitigate these spurious correlations. Our method enforces counterfactual invariance, ensuring reward predictions remain consistent when irrelevant variables are altered. Through experiments on both synthetic and real-world datasets, we show that our approach mitigates various types of spurious correlations effectively, resulting in more reliable and fair alignment of LLMs with human preferences. As a drop-in enhancement to the existing RLHF workflow, our causal reward modeling provides a practical way to improve the trustworthiness and fairness of LLM finetuning. |
| title | Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.09620 |